A HOG-based hand gesture recognition system on a mobile device

We propose a HOG-based hand gesture recognition system running on a mobile device. Input data is a video of hand gesture taken by a mobile device. The input data is compared with a database storing hand gesture images, which was synthesized with rotation variation. The comparison is done based on their HOG features and the gesture corresponding to the best-matched image is returned as the result. The recognition algorithm is implemented on a client-server system. The proposed system is applied to American Sign Language (ASL) alphabet recognition problem. The experimental results show that the proposed recognition algorithm improves HOG´s robustness under rotation change and compare processing time with different network configurations.